Classification trees: an alternative to traditional land cover classifiers. Image classification is a complex process that may be affected by many factors. Literature Survey Reference Paper - 04 15. Similarly, incorporating ancillary data in a classification procedure is an effective way to improve classification accuracy. These convolutional neural network models are ubiquitous in the image data space. Classification of SPOT HRV imagery and texture features. Temporal resolution refers to the time interval in which a satellite revisits the same location. An object‐specific image‐texture analysis of H‐resolution forest imagery. Classification of Mediterranean crops with multisensor data: per‐pixel versus per‐object statistics and image segmentation. A comparison of contextual classification methods using Landsat TM. Multitemporal land‐cover classification using SIR‐C/X‐SAR imagery. For a particular study, it is often difficult to identify the best classifier due to the lack of a guideline for selection and the availability of suitable classification algorithms to hand. Spectral unmixing of hyperspectral imagery for mineral exploration: comparison of results from SFSI and AVIRIS. Modified kappa coefficient and tau coefficient have been developed as improved measures of classification accuracy (Foody 1992, Ma and Redmond 1995). neural network, decision tree), have their own strengths and limitations (Tso and Mather 2001, Franklin et al. 1990, Meyer et al. 2. Here preprocess is done before feature extraction. 2003, Pal and Mather 2003, Erbek et al. An iterative approach to partially supervised classification problems. They work phenomenally well on computer vision tasks like image classification, object detection, image recogniti… Thus, ancillary data are often used to modify the classification image based on established expert rules. Remotely sensed data as an information source for geographical information systems in natural resource management: a review. The output of SMA is typically presented in the form of fraction images, with one image for each endmember spectrum, representing the area proportions of the endmembers within the pixel. Gong et al. An alternate approach is to use an object‐oriented classification (Thomas et al. 2003, Benz et al. A Markov random field model for classification of multisource satellite imagery. A combination of multisensor data with various image characteristics is usually beneficial to the research (Lefsky and Cohen 2003). The rest of the paper is designed as follows: Section 2 details a literature survey. Selecting and interpreting measures of thematic classification accuracy. The emphasis is placed on the summarization of major advanced classification approaches and the techniques used for improving classification accuracy. 2001, Magnussen et al. Rotational transformation of remotely sensed data for land use classification. 1998b, Rashed et al. ICA mixture models for unsupervised classification of non‐gaussian classes and automatic context switching in blind signal separation. 2001, Dungan 2002). (2004) identified three major problems when medium spatial resolution data are used for vegetation classifications: defining adequate hierarchical levels for mapping, defining discrete land‐cover units discernible by selected remote‐sensing data, and selecting representative training sites. 1996, Jakubauskas 1997, Nyoungui et al. Endmember selection for multiple endmember spectral mixture analysis using endmember average RMSE. 2003) and is especially important for improving area estimation of land‐cover classes based on coarse spatial resolution data. Different classification methods have their own merits. Successful classification of images results in filtering out irrelevant images which improves the performance of such systems. 2004). (1999), and Foody (2002b), have conducted reviews on classification accuracy assessment. The emphasis is placed on the summarization of major advanced classification approaches and the techniques used for improving classification accuracy. Classification of remotely sensed data by an artificial neural network: issues related to training data characteristics. If a single‐date image is used in classification, atmospheric correction may not be required (Song et al. 1998a, Mustard and Sunshine 1999, Lu et al. Evidential reasoning with Landsat TM, DEM and GIS data for land cover classification in support of grizzly bear habitat mapping. 1985, Cushnie 1987). These disadvantages may lower classification accuracy if classifiers cannot effectively handle them (Irons et al. Linear mixing and the estimation of ground cover proportions. Use of normalized difference built‐up index in automatically mapping urban areas from TM imagery. An integrated approach to land cover classification: an example in the Island of Jersey. As various sensor data with different resolutions emerge, remote sensing/GIS integration may provide new insights in image classification for its capability in handling the scale issue. At a regional scale, medium spatial resolution data such as Landsat TM/ETM+, and Terra ASTER are the most frequently used data. Classification of hyperdimensional data based on feature and decision fusion approaches using projection pursuit, majority voting, and neural networks. Comparison and testing of different classification algorithms for various applications are also necessary. A quantitative assessment of a combined spectral and GIS rule‐based land‐cover classification in the Neuse river basin of North Carolina. Comparison of IKONOS and QuickBird images for mapping mangrove species on the Caribbean coast of panama. Automatic land cover analysis for Tenerife by supervised classification using remotely sensed data. Random crops of 227 × 227 pixels from the input image of size 256 × 256; Randomly mirror images in each forward-backward training pass; When predicting, the network expects face image, cropped to 227 × 227 around the face center. Many potential variables may be used in image classification, including spectral signatures, vegetation indices, transformed images, textural or contextual information, multitemporal images, multisensor images, and ancillary data. 2001, Du et al. Remote sensing and geographic information systems: towards integrated spatial information processing. In addition to elevation, slope and aspect derived from DEM data have also been employed in image classification. Mixture density separation as a tool for high‐quality interpretation of multi‐source remote sensing data and related issues. 2002, Pal and Mather 2003, Gallego 2004). Hyperspectral mixture modeling for quantifying sparse vegetation cover in arid environments. 1997, Stehman 1996, 1997, Congalton and Green 1999, Smits et al. A regional measure of abundance from multispectral images. 1994, Wang and Civco 1994), knowledge‐based techniques (Srinivasan and Richards 1990, Amarsaikhan and Douglas 2004), fuzzy contextual classification (Binaghi et al. 2002a, Guerschman et al. A framework for the modeling of uncertainty between remote sensing and geographic information systems. As spaceborne hyperspectral data such as EO‐1 Hyperion become available, research and applications with hyperspectral data will increase. Landsat TM) (Yocky 1996, Shaban and Dikshit 2002) in order to enhance the information contents from both datasets. Fusion of airborne polarimetric and interfermetric SAR data for classification of coastal environments. 2004). Multisensor integration and fusion for intelligent systems. The methods, including colour‐related techniques (e.g. Optimal selection of spectral bands for classifications has been extensively discussed in previous literature (Mausel et al. A new supervised classification method for quantitative analysis of remotely sensed multi‐spectral data. II. Integrated analysis of spatial data from multiple sources: an overview. GIS plays an important role in developing knowledge‐based classification approaches because of its capability of managing different sources of data and spatial modelling. Estimating the Kappa coefficient and its variance under stratified random sampling. 2003). Incorporating ancillary data into a logical filter for classified satellite imagery. A comparison of spatial feature extraction algorithms for land‐use classification with SPOT HRV data. 2002). Classification by progressive generalization: a new automated methodology for remote sensing multispectral data. Approaches to fractional land cover and continuous field mapping: a comparative assessment over the BOREAS study region. Different classifiers, such as parametric classifiers (e.g. A robust texture analysis and classification approach for urban land‐use and land‐cover feature discrimination. They have assessed the status of accuracy assessment of image classification, and discussed relevant issues. Similarly, temperature, precipitation, and soil data are related to land‐cover distribution at a large scale. Land cover mapping in an agricultural setting using multiseasonal Thematic Mapper data. Merging of IRS LISS III and PAN data—evaluation of various methods for a predominantly agricultural area. Use of topographic correction in Landsat TM‐based forest interpretation in Nepal. An overview of uncertainty in optical remotely sensed data for ecological applications. Spatial metrics and image texture for mapping urban land use. [5], the paper studies the development of Deep CNN (Convolutional Neural Network) and to match its image classification performance with the performance of the dermatologists. Object‐based image classification for burned area mapping of Creus Cape Spain, using NOAA‐AVHRR imagery. Multiresolution wavelet decomposition image merger of Landsat Thematic Mapper and SPOT panchromatic data. The foci of this paper are on providing a summarization of major advanced classification methods and techniques used for improving classification accuracy, and on discussing important issues affecting the success of image classifications. A physically‐based model to correct atmospheric and illumination effects in optical satellite data of rugged terrain. 2003). The number of spectral bands used for image classification can range from a limited number of multispectral bands (e.g. The original ALL image and sheared image. Most classification approaches are based on per‐pixel information, in which each pixel is classified into one category and the land‐cover classes are mutually exclusive. Abstract: Deep convolutional neural networks (CNN) have led to a successful breakthrough for hyperspectral image (HSI) classification. In reality, no classification algorithm can satisfy all these requirements nor be applicable to all studies, due to different environmental settings and datasets used. Change identification using multitemporal spectral mixture analysis: applications in eastern Amazonia. 2000, Schmidt et al. More research, however, is needed to identify and reduce uncertainties in the image‐processing chain to improve classification accuracy. Global land cover classification at 8 km spatial resolution: the use of training data derived from Landsat imagery in decision tree classifiers. 1999), and decision (Benediktsson and Kanellopoulos 1999). IKONOS imagery for resource management: tree cover, impervious surfaces, and riparian buffer analyses in the mid‐Atlantic region. image retrieval has become an increasingly important area in computer vision and multimedia computing. Document image classification is an important step in Office Automation, Digital Libraries, and other document image analysis applications. 1997, 1999) have been used for classification of multisource data. Congalton and Green (1999) systematically reviewed the concept of basic accuracy assessment and some advanced topics involved in fuzzy‐logic and multilayer assessments, and explained principles and practical considerations in designing and conducting accuracy assessment of remote‐sensing data. A survey of image classification methods .... 5. Spatial variation in land cover and choice of spatial resolution for remote sensing. Classification using ASTER data and SVM algorithms: the case study of Beer Sheva, Israel. Presentation Outline • INTRODUCTION • LITERATURE SURVEY • EXAMPLES • METHADOLOGY • EXPERIMENTS • RESULTS • CONCLUSION AND FUTURE WORK • REFERENCES 3. Artificial neural networks (ANNs) are now widely used by researchers, but their operational applications are hindered by the need for the user to specify the configuration of the network architecture and to provide values for a number of parameters, both of which affect performance. 2004, Hadjimitsis et al. Another potential approach is to use multiscale data to implement calibration of classification results through modelling. Rationale and conceptual framework for classification approaches to assess forest resources and properties. A critical step is to develop the rules that can be used in an expert system or a knowledge‐based classification approach. The interactive effect of spatial resolution and degree of internal variability within land‐cover types on classification accuracies. 2004) and a support vector machine (Kim et al. 1993, Richter 1997, Gu and Gillespie 1998, Hale and Rock 2003). Evaluation of the merging of SPOT multispectral and panchromatic data for classification of an urban environment. Mapping boreal vegetation using Landsat TM and topographic map data in a stratified approach. 1997, Gahegan and Ehlers 2000, Crosetto et al. Similarly, recreational grass is often found in residential areas, but pasture and crops are largely located away from residential areas, with sparse houses and a low population density. The long‐wavelength radar data can penetrate the canopy structure to a certain depth and can provide information on vegetation stand structures (Leckie 1998, Santos et al. 1990, Maselli et al. Moreover, image data have been integrated with ancillary data as another means for enhancing image classification. Data fusion or integration of multisensor or multiresolution data takes advantage of the strengths of distinct image data for improvement of visual interpretation and quantitative analysis. Textural and contextual land‐cover classification using single and multiple classifier systems. The presence of mixed pixels has been recognized as a major problem, affecting the effective use of remotely sensed data in per‐pixel classifications (Fisher 1997, Cracknell 1998). Understanding the strengths and weaknesses of different types of sensor data is essential for the selection of suitable remotely sensed data for image classification. Remotely sensed data, including both airborne and spaceborne sensor data, vary in spatial, radiometric, spectral, and temporal resolutions. This algorithm has almost similar , at times even better, runtime and randomness than some of the existing algorithms like DES. 2004). 2003, Landgrebe 2003, Platt and Goetz 2004) may be used for feature extraction, in order to reduce the data redundancy inherent in remotely sensed data or to extract specific land‐cover information. Knowledge‐based techniques for multisource classification. Uncertainty may be modelled or quantified in different ways such as fuzzy and probabilistic classification techniques, or via visualization (van der Wel et al. Mapping deciduous forest ice storm damage using Landsat and environmental data. A texture enhancement procedure for separating orchard from forest in Thematic Mapper imagery. An overall classification accuracy of 88% was achieved from multitemporal images compared to 69% from single‐date imagery. The spectral value of each pixel is assumed to be a linear or non‐linear combination of defined pure materials (or endmembers), providing proportional membership of each pixel to each endmember. theory for transmission line icing image. Different approaches have been developed to reduce the impact of the mixed pixel problem. Delineation of forest/nonforest land use classes using nearest neighbor methods. Evaluation of uncertainties caused by the use of multisource data is becoming an important research topic. Friedl et al. Evaluation of the grey‐level co‐occurrence matrix method for land‐cover classification using SPOT imagery. An investigation of the textural characteristics associated with gray level cooccurrence matrix statistical parameters. 1988, Ekstrand 1996, Richter 1997, Gu and Gillespie 1998, Dymond and Shepherd 1999, Tokola et al. Application of multi‐temporal Landsat 5 TM imagery for wetland identification. Radiometric and atmospheric calibrations are also needed before multisensor data are merged. Literature survey image processing Computer vision researchers have long been trying to propose methods for visual sorting and grading of fruits. Alternative criteria for defining fuzzy boundaries based on fuzzy classification of aerial photographs and satellite images. The fraction images are related to biophysical characteristics, and thus have the potential for improving classification (Roberts et al. Data over tropical vegetation the anomalies to combine the classification image based on subpixel sun‐canopy‐sensor geometry of coniferous forests AVIRIS... That integration of classification results has gained some attention recently ( McIver and Friedl 2001, et... Calibration approaches based on feature and decision ( Benediktsson and Kanellopoulos, )... Spatial detail that can be per‐pixel, subpixel classifier for urban delineation: a review of current in. 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